Extensive red blood cell matching considering patient alloimmunization risk

BACKGROUND AND OBJECTIVES

Red blood cell (RBC) transfusions pose a risk of alloantibody development in patients. For patients with increased alloimmunization risk, extended preventive matching is advised, encompassing not only the ABO-D blood groups but also the most clinically relevant minor antigens: C, c, E, e, K, Fya, Fyb, Jka, Jkb, S and s. This study incorporates patient-specific data and the clinical consequences of mismatching into the allocation process.

MATERIALS AND METHODS

We have redefined the MINimize Relative Alloimmunization Risks (MINRAR) model to include patient group preferences in selecting RBC units from a finite supply. A linear optimization approach was employed, considering both antigen immunogenicity and the clinical impact of mismatches for specific patient groups. We also explore the advantages of informing the blood bank about scheduled transfusions, allowing for a more strategic blood distribution. The model is evaluated using historical data from two Dutch hospitals, measuring shortages and minor antigen mismatches.

RESULTS

The updated model, emphasizing patient group-specific considerations, achieves a similar number of mismatches as the original, yet shifts mismatches among patient groups and antigens, reducing expected alloimmunization consequences. Simultaneous matching for multiple hospitals at the distribution centre level, considering scheduled demands, led to a 30% decrease in mismatches and a 92% reduction in shortages.

CONCLUSION

The reduction of expected alloimmunization consequences by incorporating patient group preferences demonstrates our strategy's effectiveness for patient health. Substantial reductions in mismatches and shortages with multi-hospital collaboration highlights the importance of sharing information in the blood supply chain.

A linear optimization model for high dose rate brachytherapy using a novel distance metric

Purpose.We propose a linear network-based optimization model (LNBM) for high dose rate brachytherapy (HDR-BT) that uses a novel distance metric to measure the discrepancy between the dose delivered and the prescription. Unlike models in the literature, LNBM takes advantage of the adjacency structure of the patients' voxels by formalizing them into a network.Methods.We apply LNBM to a set of 7 cervical cancer cases treated with HDR-BT. State-of-the-art commercial optimization software solves LNBM to global optimality. The results of LNBM are compared with those of inverse planning by simulated annealing (IPSA) based on tumor coverage, dosimetric indices for the critical organs at risk (OARs), isodose contour plots, and two metrics of homogeneity new to this work (hot-spots volumes and diameters).Results.LNBM produces plans with improved tumor coverage and with improved isodose contour plots and dosimetric indices for OARs that receive highest dose (bladder and rectum in this study) when compared with IPSA. Using new metrics of homogeneity, we also demonstrate that LNBM produces more homogeneous plans on these cases. An analysis of the solutions of LNBM shows that they use a significant part of the voxel network structure, providing evidence that the plans produced are different from those created using traditional penalty approaches and are more directly guided by the geometry of the patients' anatomy.Conclusions.The proposed linear network-based optimization model efficiently generates more homogeneous high quality treatment plans for HDR-BT.

Optimization of Protein Quality of Plant-Based Foods Through Digitalized Product Development

With the increasing availability of plant-based protein products that should serve as alternatives to animal-based protein products, it is necessary to develop not only environmentally friendly but also nutritious foods. Especially the protein content and quality are of concern in these products. The algorithm of NutriOpt was developed using linear programming to support the development of food products with a balanced amino acid profile while considering digestibility. The current version contains a database with 84 plant protein sources from different food groups (legumes, cereals, nuts, seeds) and with different grades of purification (flours, concentrates, isolates) from which NutriOpt can create mixtures with high protein quality while complying with constraints such as protein content, number of ingredients, and weight of the mixture. The program was tested through different case studies based on commercial plant-based drinks. It was possible to obtain formulations with a Protein Digestibility Corrected Amino Acid Score (PDCAAS) over 100 with ingredients and quantities potentially suitable for plant-based analogs. Our model can help to develop the second generation of plant-based product alternatives that can really be used as an alternative on long-term consumption. Further, there is still a great potential of expansion of the program for example to use press cakes or even to model whole menus or diets in the future.

The impact of public acceptance on cost efficiency and environmental sustainability in decentralized energy systems

Local resistance often hinders renewable energy technology developments, especially for onshore wind. In decentralized energy systems, the landscape impact of wind turbines or transmission lines is a key barrier to public acceptance. By using landscape scenicness as a proxy for public acceptance, we quantify its impact on the optimal energy systems of 11,131 German municipalities. In municipalities with high scenicness, it is likely that onshore wind will be rejected, leading to higher levelized costs of energy by up to about 7 €-cent/kWh. Onshore wind would be replaced mainly by solar photovoltaics and imports, and the cost-optimal energy systems would be associated with higher CO₂ emissions of up to about 200 gCO₂/kWh compared with an average of around 50 gCO₂/kWh. The findings help to identify municipalities where public resistance to onshore wind could be particularly high and support the scientific and policy debate about the location of onshore wind farms.

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Determine cost-optimal energy systems of 11,131 German municipalities in 2050

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Combine cluster and regression analyses with mathematical optimization methods

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Apply nationwide scenicness data to estimate the landscape impact of onshore wind

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Rejecting onshore wind leads to a significant increase in costs and CO₂ emissions

Renewable energy technologies are necessary to maintain secure energy supplies and limit the impacts of climate change. Developments of these technologies are mostly planned purely based on economic criteria, but this can lead to resistance in local communities. Among the diverse renewable technologies, especially onshore wind turbines may negatively affect the scenicness of beautiful landscapes. We analyze how cost-efficient local energy systems could be impacted through public opposition toward onshore wind. In doing so, we draw on a database of public evaluation of landscape beauty across Germany. In the energy systems of German municipalities with high scenicness, onshore wind would mainly be replaced by solar photovoltaics. Depending on the location, the local energy systems may be associated with a significant increase in costs and CO₂ emissions. These insights can support local and national stakeholders in making decisions relating to energy and climate policy.